What the data for this use case looks like
The data processed for this use case comes from the budget management integrated system, project submission materials, fund payment data, business system ledgers, and satisfaction survey results. The update frequency is annual. Daily processed documents include performance target forms, self-assessment forms, performance evaluation reports, supporting material lists, and rectification reports. Data fields include project number, fund association information, performance indicator completion status, supporting material association records, and more. It is necessary to link and match fund expenditure flow with performance target achievement status. The annual document volume increases with the number of evaluated projects.
Constraints for Deployment and Upgrade
The annual update rhythm requires support for batch historical data import and incremental synchronization during deployment, to avoid reprocessing past data. Multi-source data docking needs configuration of multi-data source adaptation capabilities, compatible with interface formats of different systems. Diverse document formats require configuration of general parsing adaptation logic, covering PDF, tables, text and other supporting material types. Severe consequences of errors require adding data verification and exception alert links during deployment, to prevent incorrect data from entering the evaluation process. The upgrade process must be compatible with the original ledger data structure, to avoid interrupting the established annual evaluation workflow.
Configuration Recommendations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 100–200 records/minute | Adapts to the reading frequency of annual batch ledger data, avoids exhausting database connections |
MONGODB_URI | Points to a locally privately deployed MongoDB instance | Stores unstructured data associated with performance evaluation reports and supporting materials |
REDIS_URL | Configured as the address of a local Redis cluster, with memory no less than 2 GB | Caches frequently accessed performance indicator data and task status, improves aggregation efficiency |
STORAGE_S3_ENDPOINT | Bound to the internal unit object storage service endpoint | Stores large volumes of supporting materials and historical evaluation documents |
FILE_TOKEN_KEY | Custom 32-byte random secret key | Generates authentication credentials for ledger sharing links, restricts unauthorized access |
ROOT_KEY | Only configured for operations administrator accounts | Controls system configuration modification permissions during deployment and upgrades, prevents misoperations |
Three Common Mistakes
- Ledger sharing links lack identity verification, accessible to any user. Cause: The
FILE_TOKEN_KEYparameter is not configured, or link authentication logic is not enabled. - Timeouts or parsing failures occur when parsing PDF supporting materials. Cause: The marker Docker image is not built correctly, and insufficient video memory resources are reserved for document parsing.
- Abnormal number of vector recall results appear when connecting to the bge-m3 model privately deployed on GPUStack. Cause: The model docking endpoint and permissions are not configured correctly, and the input and output format requirements of the model are not matched.
How to Confirm Correct Configuration
- On FastGPT v4.17.0, upload a single performance self-assessment form, verify that core fields such as project number and fund association information are correctly extracted after document parsing.
- Initiate an annual ledger aggregation task, check whether there are high-frequency data write records in the cache instance corresponding to
REDIS_URL, confirm that caching is effective. - Generate a performance evaluation report sharing link, use an unauthorized account to access, verify that a no access permission prompt is displayed.
- Perform a system upgrade operation, check whether original historical performance data can be normally queried and exported, confirm data compatibility.
The values provided here are common starting points, and should be evaluated against the user's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.